PR #23: 3D Pose Estimation

Added 3d pose estimation file
Merge pull request #23 from mahipalimkar/Mahi

3d_pose_estimation/3d_pose_estimation.py

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+import cv2
+import mediapipe as mp
+import numpy as np
+import json
+import socket
+
+# Set up the UDP socket
+sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+server_address = ('localhost', 12345)  # Port for Unity to listen to
+
+# Function to read and return 3D landmark positions
+def read_landmark_positions_3d(results):
+    if results.pose_world_landmarks is None:
+        return None
+    else:
+        # Extract 3D landmark positions
+        landmarks = [results.pose_world_landmarks.landmark[lm] for lm in mp.solutions.pose.PoseLandmark]
+        return np.array([(lm.x, lm.y, lm.z) for lm in landmarks])
+
+# Function to draw landmarks on the image
+def draw_landmarks_on_image(frame, results):
+    if results.pose_landmarks is not None:
+        mp_drawing = mp.solutions.drawing_utils
+        mp_pose = mp.solutions.pose
+        mp_drawing.draw_landmarks(
+            frame,
+            results.pose_landmarks,
+            mp_pose.POSE_CONNECTIONS,
+            mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2),
+            mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2),
+        )
+
+# Real-time 3D pose estimation function
+def real_time_pose_estimation():
+    # Initialize webcam or video
+    cap = cv2.VideoCapture(0)  # Use 0 for webcam
+
+    # Initialize Mediapipe Pose model
+    mp_pose = mp.solutions.pose
+    pose_detector = mp_pose.Pose(static_image_mode=False, model_complexity=2)
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Convert the frame to RGB (required by Mediapipe)
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Process the frame to obtain pose landmarks
+        results = pose_detector.process(frame_rgb)
+
+        # Draw landmarks on the frame (optional for visualization)
+        draw_landmarks_on_image(frame, results)
+
+        # Extract 3D landmarks
+        landmark_positions_3d = read_landmark_positions_3d(results)
+        if landmark_positions_3d is not None:
+            # Send landmark positions to Unity via UDP
+            data = json.dumps(landmark_positions_3d.tolist())  # Convert to JSON format
+            sock.sendto(data.encode('utf-8'), server_address)  # Send data to Unity
+            print(f'3D Landmarks: {landmark_positions_3d}')  # Optional: Print landmarks to console
+
+        # Display the frame with landmarks drawn
+        cv2.imshow('Real-Time 3D Pose Estimation', frame)
+
+        # Exit loop when 'q' key is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    real_time_pose_estimation()

3d_pose_estimation/README.md

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+# Real-Time 3D Pose Estimation
+
+This project implements real-time 3D pose estimation using MediaPipe and OpenCV. It captures video from a webcam, detects human pose landmarks in 3D, and sends the landmark data to a Unity application via UDP.
+
+## Table of Contents
+- [Features](#features)
+- [Requirements](#requirements)
+
+
+## Features
+- Real-time detection of 3D pose landmarks using MediaPipe.
+- Visualization of detected landmarks on the webcam feed.
+- Sending 3D landmark coordinates to a Unity application via UDP for further processing or visualization.
+- Option to terminate the program by pressing the 'q' key.
+
+## Requirements
+- Python 3.x
+- OpenCV
+- MediaPipe
+- NumPy
+- JSON
+- Socket
+